Statistical models for spatially explicit biological data

Parasitology

Volumn 139, Issue 14, 2012, Pages 1852-1869

  Rogers, David J ^a   Sedda, Luigi ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

bluetongue; cokriging; kriging; Species' distribution models; variograms

Indexed keywords

ALGORITHM; ARTICLE; BLUETONGUE; BLUETONGUE ORBIVIRUS; COVARIANCE; DATA ANALYSIS; DATA ANALYSIS SOFTWARE; EPIDEMIC; FUZZY LOGIC; GEOSTATISTICAL ANALYSIS; HISTOGRAM; INFORMATION PROCESSING; KAPPA STATISTICS; KRIGING; LOGISTIC REGRESSION ANALYSIS; MULTIVARIATE ANALYSIS; NONHUMAN; PREDICTION; PRIORITY JOURNAL; RANDOM ERROR; SAMPLING; SENSITIVITY AND SPECIFICITY; SPECIES DISTRIBUTION; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; ANIMALS; BLUETONGUE; COMPUTER SIMULATION; DEMOGRAPHY; ENVIRONMENT; EPIDEMIOLOGIC METHODS; EUROPE; MODELS, STATISTICAL; SPATIAL ANALYSIS;

EID: 84870504556     PISSN: 00311820     EISSN: 14698161     Source Type: Journal    
DOI: 10.1017/S0031182012001345     Document Type: Article

References (80)

1. Allouche, O., Tsoar, A. and Kadmon, R. (2006). Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43, 1223-1232. doi: 10.1111/j.1365-2664.2006. 01214.x. (Pubitemid 44665297)
2. Atkinson, P. M., Webster, R. and Curran, P. J. (1994). Cokriging with Airborne MSS Imagery. Remote Sensing of Environment 50, 335-345. doi: 10.1016/0034-4257(94)90083-3.
3. Austin, M. (2007). Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modelling 200, 1-19. doi: 10.1016/j.ecolmodel.2006.07.005. (Pubitemid 44828248)
4. Austin, M. P. and Van Niel, K. P. (2011). Improving species distribution models for climate change studies: variable selection and scale. Journal of Biogeography 38, 1-8. doi: 10.1111/j.1365- 2699.2010.02416.x.
5. Bachmaier M and Backes, M. (2011). Variogram or semivariogram? Variance or semivariance? Allan variance or introducing a new term? Mathematical Geology 43, 735-740. doi: 10.1007/s11004-011-9348-3.
6. Bellier, E., Monestiez, P., Durbec, J. P. and Candau, J. N. (2007). Identifying spatial relationships at multiple scales: principal coordinates of neighbour matrices (PCNM) and geostatistical approaches. Ecography 30, 385-399. doi: 10.1111/j.2007.0906-7590.04911.x. (Pubitemid 46889886)
7. Ben-Ahmed, K., Bouratbine, A. and El-Aroui, M. A. (2010). Generalized linear spatial models in epidemiology: A case study of zoonotic cutaneous leishmaniasis in Tunisia. Journal of Applied Statistics 37, 159-170. doi: 10.1080/02664760802684169.
8. Berke, O. (2004). Exploratory disease mapping: kriging the spatial risk function from regional count data. International Journal of Health Geographics 3:18, 1-11. doi: 10.1016/j.prevetmed.2005.07.003.
9. Breiman, L. (1984). Classification and Regression Trees. Wadsworth International Group, Belmont, Calif.
10. Breiman, L. (2001). Random forests. Machine Learning 45, 5-32. doi: 10.1023/A:1010933404324. (Pubitemid 32933532)
11. Carrat, F. and Valleron, A. J. (1992). Epidemiologic mapping using the "kriging" method: Application to an influenza-like illness in France. American Journal of Epidemiology 135, 1293-1300.
12. Chiogna, M. and Gaetan, C. (2010). An interchangeable approach for modelling spatio-temporal count data. Environmetrics 21, 844-862. doi: 10.1002/Env.1078.
13. Christakos, G. (2000). Modern Spatiotemporal Geostatistics. I Edn. Oxford University Press, Oxford.
14. Christensen, R. (2001). Advanced Linear Modeling : Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization, 2nd edn., Springer, New York.
15. Congalton, R. G. (1991). A review assessing the accuracy of classification of remotely sensed data. Remote Sensing and Environment 37, 35-46. doi: 10.1016/0034-4257(91)90048-B.
16. Crase, B., Liedloff, A. C. and Wintle, B. A. (2012). A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography 35, 1-10. doi: 10.1111/j.1600- 0587.2011.07138.x
17. De Iaco, S., Maggio, S., Palma, M. and Posa, D. (2012). Towards an automatic procedure for modeling multivariate space-time data. Computers & Geosciences 41, 1-11. doi: 10.1016/j.cageo.2011.08.008.
18. de Koeijer, A.A., Boender, G. J., Nodelijk, G., Staubach, C., Meroc, E. and Elbers, A. R.W. (2011). Quantitative analysis of transmission parameters for bluetongue virus serotype 8 in Western Europe in 2006. Veterinary Research 42. doi 10.1186/1297-9716-42-53.
19. Deutsch, C. V. and Journel, A. G. (1998). GSLIB Geostatistical Software Library and User's Guide, 2nd edn. Oxford University Press, New York.
20. Diggle, P. J. (2003). Statistical Analysis of Spatial Point Patterns, 2nd edn. Arnold, London.
21. Diggle, P. J., Tawn, J. A. and Moyeed, R. A. (1998). Model-based geostatistics. Journal of the Royal Statistical Society Series C-Applied Statistics 47, 299-326. doi: 10.1111/1467-9876.00113. (Pubitemid 128076703)
22. Dormann, C. F., McPherson, J. M., Araujo, M. B., Bivand, R., Bolliger, J., Carl, G., Davies, R. G., Hirzel, A., Jetz, W., Kissling, W. D., Kuhn, I., Ohlemuller, R., Peres-Neto, P. R., Reineking, B., Schroder, B., Schurr, F. M. and Wilson, R. (2007). Methods to account for spatial autocorrelation in the analysis of species distributional data: A review. Ecography 30, 609-628. doi: 10.1111/j.2007.0906-7590.05171.x. (Pubitemid 350060576)
23. Dunn, M. R. (1983). A simple sufficient condition for a variogram model to yield positive variances under restrictions. Journal of the International Association for Mathematical Geology 15, 553-564. doi: 10.1007/BF01031177.
24. Ecker, M. D. and Gelfand, A. E. (1997). Bayesian variogram modelling for an isotropic spatial process. Journal of Agricultural, Biological, and Environmental Statistics 2, 347-369. (Pubitemid 127478510)
25. Elith, J., Graham, C. H., Anderson, R. P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R. J., Huettmann, F., Leathwick, J. R., Lehmann, A., Li, J., Lohmann, L. G., Loiselle, B. A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J. M., Townsend Peterson, A., Phillips, S. J., Richardson, K., Scachetti-Pereira, R., Schapire, R. E., Soberon, J., Williams, S., Wisz, M. S. and Zimmermann, E. (2006). Novel methods improve predictions of species' distributions from occurrence data. Ecography 29, 129-151. doi: 10.1111/j.2006.0906-7590.04596.x
26. Elith, J., Kearney,M. and Phillips, S. (2010). The art of modelling rangeshifting species. Methods in Ecology and Evolution 1, 330-342. doi: 10.1111/j.2041-210X.2010.00036.x.
27. Elith, J., Leathwick, J. R. and Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology 77, 802-813. doi: 10.1111/j.1365-2656.2008.01390.x. (Pubitemid 351799674)
28. Fortin, M.-J. and Dale, M. R. T. (2005). Spatial Analysis : A Guide for Ecologists, Cambridge University Press, Cambridge.
29. Franklin, J. and Miller, J. A. (2009). Mapping Species Distributions : Spatial Inference and Prediction, Cambridge University Press, Cambridge.
30. Furrer, R., Nychka, D. and Sain, S. (2012). fields: Tools for spatial data. R package version 6.01, URL http://CRAN.R-project.org/package=fields.
31. Gaetan, C. and Guyon, X. (2010). Spatial Statistics and Modeling. Springer, New York, London.
32. Gerbier, G., Baldet, T., Tran, A., Hendrickx, G., Guis, H., Mintiens, K., Elbers, A. R.W. and Staubach, C. (2008). Modelling local dispersal of bluetongue virus serotype 8 using random walk. Preventive Veterinary Medicine 87, 119-130. doi: 10.1016/j.prevetmed. 2008.06.012.
33. Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press, New York.
34. Goovaerts, P. and Kerry, R. (2010). Using ancillary data to improve prediction of soil and crop attributes in precision agriculture. In Geostatistical Applications for Precision Agriculture (ed. Oliver, M. A.), pp. 167-194. Springer Science+Business Media B.V., Dordrecht.
35. Graham, M. H. (2003). Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809-2815. (Pubitemid 38261430)
36. Guisan, A., Edwards, T. C. and Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 157, 89-100. doi: 10.1016/S0304-3800(02) 00204-1. (Pubitemid 35255408)
37. Guisan, A. and Thuiller, W. (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letters 8, 993-1009. doi: 10.1111/j.1461-0248.2005.00792.x. (Pubitemid 41247275)
38. Hampton, KH., Serre, M. L., Gesink, D. C., Pilcher, C. D. and Miller, W. C. (2011). Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping. International Journal of Health Geographics 10:54. doi 10.1186/1476-072x-10-54.
39. Hastie, T. and Tibshirani, R. (1986). Generalized additive models. Statistical Science 1, 297-318.
40. Hendrickx, G., Gilbert, M., Staubach, C., Elbers, A., Mintiens, K., Gerbier, G. and Ducheyne, E. (2008). A wind density model to quantify the airborne spread of Culicoides species during North-Western Europe bluetongue epidemic, 2006. Preventive Veterinary Medicine 87, 162-181. doi: 10.1016/j.prevetmed.2008.06.009.
41. Hoeting, J. A., Davis, R. A., Merton, A. A. and Thompson, S. E. (2006). Model selection for geostatistical models. Ecological Applications 16, 87-98. doi: 10.1890/04-0576.
42. Isaaks, E. H. and Srivastava, R. M. (1989). An Introduction to Applied Geostatistics. Oxford University Press, Oxford.
43. Journel, A. G. and Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London.
44. Journel, A.G. and Rossi, M. E. (1989). When do we need a trend model in kriging? Mathematical Geology 21, 715-739. doi: 10.1007/BF00893318.
45. Justice, C., Townshend, J., Vermote, E., Sohlberg, R., Descloitres, J., Roy, D., Hall, D., Salomonson, V., Riggs, G., Huete, A., Didan, K., Miura, T., Wan, Z. M., Strahler, A., Schaaf, C., Myneni, R., Running, S., Glassy, J., Nemani, R., El Saleous, N. and Wolfe, R. (2000). Preliminary land surface products from the NASA moderate resolution imaging spectroradiometer (MODIS). IGARSS 2000: IEEE 2000 International Geoscience and Remote Sensing Symposium, Vol I-Vi, Proceedings 1157-1162.
46. Kelsall, J. and Wakefield, J. (2002). Modeling spatial variation in disease risk: A geostatistical approach. Journal of the American Statistical Association 97, 692-701. doi: 10.1198/016214502388618438.
47. Kravchenko, A. N. (2003). Influence of spatial structure on accuracy of interpolation methods. Soil Science Society of America Journal 67, 1564-1571. doi: 10.2136/sssaj2003.1564. (Pubitemid 37143589)
48. Krige, D.G. (1951). A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of the Chemistry, Metallurgical and Mining Society of South Africa 52, 119-139.
49. Lai, P. C., So, F. M. and Chan, K.W. (2009). Spatial Epidemiological Approaches in Disease Mapping and Analysis, CRC Press, Boca Raton.
50. Latimer, A. M., Wu, S., Gelfand, A. E. and Silander, J. A. (2006). Building statistical models to analyze species distributions. Ecological Applications 16, 33-50. doi: 10.1890/04-0609.
51. Lennon, J. J. (2000). Red-shift and red herrings in geographical ecology. Ecography 23, 101-113. doi: 10.1111/j.1600-0587.2000.tb00265.x. (Pubitemid 30167957)
52. Li, J. and Heap, A. D. (2011). A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics 6, 228-241. doi: 10.1016/j. ecoinf.2010.12.003.
53. Liu, Y.X., Jiang, S.W., Liu, Y. X., Wang, R., Li, X., Yuan, Z. S., Wang, L. X. and Xue, F. Z. (2011). Spatial epidemiology and spatial ecology study of worldwide drug-resistant tuberculosis. International Journal of Health Geographics 10:50. doi: 10.1186/1476-072x-10-50.
54. Macarthur, R. H. (1972). Geographical Ecology. Harper & Row, New York.
55. Matheron, G. (1963). Principles of geostatistics. Economic Geology 58, 1246-1266. doi: 10.2113/gsecongeo.58.8.1246.
56. Matheron, G. (1965). Les variables régionalisées et leur estimation : une application de la théorie des fonctions aléatoires aux sciences de la nature. Masson et Cie, Paris
57. Mintiens, K., Meroc, E., Faes, C., Abrahantes, J. C., Hendrickx, G., Staubach, C., Gerbier, G., Elbers, A. R.W., Aerts, M. and De Clercq, K. (2008). Impact of human interventions on the spread of bluetongue virus serotype 8 during the 2006 epidemic in North-Western Europe. Preventive Veterinary Medicine 87, 145-161. doi: 10.1016/j. prevetmed.2008.06.010.
58. Myers, D. E. (1989). To be or not to be. . .stationary? That is the question. Mathematical Geology 21, 347-362. doi: 10.1007/BF00893695.
59. Oliver, M. A. (2010). The variogram and kriging. In Handbook of Applied Spatial Analysis: Software, Tools, Methods and Applications (eds. Fischer, M. M. and Getis, A.), pp. 319-352. Springer, Heidelberg.
60. Oliver, M. A., Muir, K. R., Webster, R., Parkes, S. E., Cameron, A. H., Stevens, M. C. G. and Mann, J. R. (1992). A Geostatistical Approach to the Analysis of Pattern in Rare Disease. Journal of Public Health Medicine 14, 280-289.
61. Pearson, R. G., Dawson, T. P., Berry, P. M. and Harrison, P. A. (2002). SPECIES: A Spatial Evaluation of Climate Impact on the Envelope of Species. Ecological Modelling 154, 289-300. doi: 10.1016/S0304-3800(02) 00056-X. (Pubitemid 34793523)
62. Pebesma, E. J. (2004). Multivariable geostatistics in S: the gstat package. Computers & Geosciences 30, 683-691. doi: 10.1016/j. cargo.2004.03.012. (Pubitemid 38994762)
63. Perez, A. M., Thurmond, M. C. and Carpenter, T. E. (2006). Spatial distribution of foot-and-mouth disease in Pakistan estimated using imperfect data. Preventive Veterinary Medicine 76, 280-289. doi: 0.1016/j. prevetmed.2006.05.013. (Pubitemid 44294717)
64. Porcu, E., Mateu, J., Zini, A. and Pini, R. (2007). Modelling spatiotemporal data: A new variogram and covariance structure proposal. Statistics and Probability Letters 77, 83-89. doi: 10.1016/j.spl.2006.05.013. (Pubitemid 44660923)
65. Ribeiro, P. J. J. and Diggle, P. J. (2001). geoR: A package for geostatistical analysis. R News 1, 15-18.
66. Rogers, D. J. (2000). Satellites, space, time and the African trypanosomiases. In Remote Sensing and Geographic Information Systems in Epidemiology (eds. Hay, S. I., Randolph, S. E. & Rogers, D. J.), pp. 129-171. Academic Press, London.
67. Rogers, D. J. (2006). Models for vectors and vector-borne diseases. Advances in Parasitology 62, 1-35. doi: 10.1016/S0065-308X(05)62001-5.
68. Rogers, D. J. and Williams, B. G. (1994). Tsetse distribution in Africa: seeing the wood and the trees. In Large-Scale Ecology and Conservation Biology. British Ecological Society Symposium XXXV (eds. Edwards, P. J., May, R. M. and Webb, N.), pp. 247-271. Blackwell Scientific Publications, Oxford.
69. Rossi, R. E., Mulla, D. J., Journel, A. G. and Franz, E. H. (1992). Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecological Monographs 62, 277-314. doi: 10.2307/2937096.
70. Sedda, L., Brown, H. E., Purse, B. V., Burgin, L., Gloster, J. and Rogers, D. J. (2012). A new algorithm quantifies the roles of wind and midge flight activity in the bluetongue epizootic in northwest Europe. Proceedings of the Royal Society B-Biological Sciences 279, 2354-2362. doi: 10.1098/rspb.2011.2555.
71. Scharlemann, J.P.W., Benz, D., Hay, S. I., Purse, B. V., Tatem, A. J., Wint, G. R.W. and Rogers, D. J. (2008). Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data. PLoS ONE 3:1. doi 10.1371/Journal. Pone.0001408.
72. Schlather, M. (2001). Simulation and analysis of random fields. R News 1, 18-20.
73. Shapiro, A. and Botha, J. D. (1991). Variogram Fitting with a General-Class of Conditionally Nonnegative Definite Functions. Computational Statistics & Data Analysis 11, 87-96. doi: 10.1016/0167- 9473(91)90055-7.
74. Souris, M. and Bichaud, L. (2011). Statistical models for bivariate spatial analysis in marked points. Examples in spatial epidemiology. Spatial and Spatio-temporal Epidemiology 2, 227-234. doi: 10.1016/j. sste.2011.06.001.
75. Sutherst, R.W. and Maywald, G. F. (1985). A computerised system for matching climates in ecology. Agriculture, Ecosystems and Environment 13, 281-299. doi: 10.1016/0167-8809.
76. Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography 46, 232-240.
77. Wagner, H. H. and Fortin, M. J. (2005). Spatial analysis of landscapes: Concepts and statistics. Ecology 86, 1975-1987. doi: 10.1890/04-0914. (Pubitemid 41117466)
78. Webster, R. and Oliver, M. A. (2007). Geostatistics for Environmental Scientists, 2nd edn. Wiley, Chichester.
79. Wilson, A. and Mellor, P. (2009). Bluetongue in Europe: vectors, epidemiology and climate change (vol 103, pg S69, 2008). Parasitology Research 104, 489-489. doi: 10.1007/s00436-008-1314-8.
80. Zimmerman, D. L. and Stein, M. (2010). Classical geostatistical methods. In Handbook of Spatial Statistics (eds. Gelfand, A. E., Diggle, P. J., Fuentes, M. and Guttorp, P.). CRC Press, Boca Raton.